COURSESINTRODUCTION TO LINEAR MODELSGeneral Information Aim: This course is predominantly an applied statistical course, with emphasis on statistical theory only when needed. It aims to provide the basic theoretical and operational concepts to the student about Linear Econometric Models of cross-section data. The course will cover estimation and inference principles, the mathematical (algebraic properties) of the Ordinary Least Square methods, simple and multiple linear regression models, tests for functional form and omitted variables, in addition to heteroskedasticity and weighted least squares. It will also emphasize the nature of residuals and analyze many of the inspection and tests of goodness-of-fit and influential measures by means of residuals. The empirical part of the course will be based on the R software and data from Wooldridge (2016). I expect that students read the suggested literature specific to linear econometrics, including the basic texts on mathematical econometrics, probability, and statistical inference, as well participate in the data laboratory classes. At the end of the course I expect students to be able to manipulate cross-section data in R and apply the methods to specific areas of interest in Demography, Geography, Sociology, Economics, and Health Studies. Tests and Grading: Assignment 1: Estimation of a simple linear regression via OLS using Excel (20 points) [Download] Assignment 2: Applied use of cross-section data to estimate, interpret and analyze the quality of the model (30 points) Final test: a formal test covering the content of the course (50 points) Tutoring: Teaching Assistants: To be updated (Doctor Student in Demography) Tutoring hours: Thursday, 11:00 am to 12:30 pm (to be updated) More details: Download the complete syllabus here. Download Data & Scripts Data Data Set Handbook. Wooldridge_RData. Datasets for Wooldridge Book (5th Edition) by Chapter on Cengage Website. Datasets for Assignment 1. Scripts Class 1 – Simulation Probability Distributions in R. Class 2 – Solution to the Computer Exercises (Chapter 1 – Wooldridge). Class 3 – How to reproduce examples throughout the chapter (Chapter 2 – Wooldridge). Class 4 – How to reproduce examples throughout the chapter (Chapter 3 – Wooldridge). Class 4 – Solution to Computer Exercises (Chapter 2 – Wooldridge). Class 4 – Frisch-Waugh-Lovell and Orthogonal Partitioned Regression Theorems (Simulation). Class 5 – How to reproduce examples throughout the chapter (Chapter 4 – Wooldridge). Class 5 – Solution to Computer Exercises (Chapter 3 – Wooldridge). Class 5 – Central Limit Theorem and the Law of Large Numbers for convergence (Simulation). Class 6 – How to reproduce examples throughout the chapter (Chapter 6 – Wooldridge). Class 6 – Solution to Computer Exercises (Chapter 4 – Wooldridge). Class 7 – How to reproduce examples throughout the chapter (Chapter 7 – Wooldridge). Class 7 – Solution to Computer Exercises (Chapter 6 – Wooldridge). Class 8 – How to reproduce examples throughout the chapter (Chapter 8 – Wooldridge). Class 8 – Solution to Computer Exercises (Chapter 7 – Wooldridge). Class 9 – Solution to Computer Exercises (Chapter 8 – Wooldridge). Writing Materials, Powerpoints & Beamers Class 1 – Review of Basic Terminology and Random Variables. Class 2 – The Nature of Econometrics and Economic Data. Class 5 – Asymptotic Theory. Compulsory Reading (Textbook) Chapter 1 – The nature of econometrics and economic data. Chapter 2 – The simple regression model. Chapter 3 – Multiple Regression Analysis: Estimation. Chapter 4 – Multiple Regression Analysis: Inference. Chapter 6 – Multiple Regression Analysis:Further Issues. Chapter 7 – Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. Chapter 8 – Heteroskedasticity. Weekly Assignments Chapter 1 – Problems and Computer Exercises. Chapter 2 – Problems and Computer Exercises. Chapter 3 – Problems and Computer Exercises. Chapter 4 – Problems and Computer Exercises. Chapter 6 – Problems and Computer Exercises. Chapter 7 – Problems and Computer Exercises. Chapter 8 – Problems and Computer Exercises. Chapter 9 – Problems and Computer Exercises. Chapter 15 – Problems and Computer Exercises. Teaching Assistant's Material Introduction to R. Basic R Manipulation (objects and basic functions). PNAD in R. Video Classes Introduction to R and R Studio. Extra Materials How to accommodate nonlinearities in LRM (variables transformation). Confidence Interval using Profile Likelihood (profile-likelihood-ci). R Markdown Cheat Sheet. Student’s material for Wooldridge’s book. Basic Mathematical Tools. Fundamentals of Probability. Fundamentals of Mathematical Statistics. Summary of Matrix Algebra. The Linear Regression Model in Matrix Form. Statistical Tables. Glossary. References Textbooks Jeffrey M. Wooldridge Introductory Econometrics: A Modern Approach, 6th Edition, CENGAGE Learning, 2012. Florian Heiss Using R for Introductory Econometrics, 1st Edition, Published using the independent publishing platform CreateSpace, 2016. Same Level Reference Books Kennedy, P. A Guide to Econometrics, Sixth Edition John Wiley & Sons, 2008. Baum, C. An Introduction to Modern Econometrics Using Stata, Stata Press 2006. Stock, J.H and M. W. Watson Introduction to Econometrics, 2nd ed., Addison-Wesley, 2006. Hill, R. Carter, Griffths, William E. and Lim, Guay C. Principles of Econometrics, 3rd ed., John Wiley & Sons, 2008. Advanced Readings Goldberger, A. S. A Course in Econometrics 1st US Edition 4th Printing Edition, Harvard University Press, 2000. Greene,W.H. Econometric Analysis, Seventh Edition, Pearson/Prentice Hall, 2012. Woodridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd Edition, MIT Press, 2010. Long, S. and J. Freese Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences), 1nd Edition, Sage Publications, 1997. Badi H. Baltagi Econometric Analysis of Panel Data, 4th Edition, Wiley, 2008. James W. Hardin, Joseph M. Hilbe Generalized Linear Models and Extensions, 2nd Edition Stata Press, 2006. Colin Cameron, Pravin K. Trivedi Regression Analysis of Count Data, Cambridge University Press, 1998. John P. Hoffmann Generalized Linear Models: An Applied Approach, Pearson, 2004. Cheng Hsiao Analysis of Panel Data, 2nd Edition, Cambridge University Press, 2003.